The physiological and metabolic differences between shoot tips and roots of tea plants are significant, and understanding them is required for improvement of tea quality and plant growth. A high-quality full-length transcriptome sequencing on tea plant roots and shoot tips by PacBio SMRT technology was done to gain a further understanding. Approximately 160699 and 166120 full-length transcripts were recovered in roots and shoots, respectively, including 31232 and 41068 novel isoforms and 16960 and 26029 alternative splicing (AS) isoforms. These supported 21699 full-length reads and 31232 and 41068 novel transcripts from root and shoot, respectively, including 1679 long noncoding RNAs (lncRNAs) and 2772 fusion transcripts, which significantly upgrade the Camellia sinensis genome annotation. The respective 6475 and 6981 transcripts in roots and shoots differ in 3'-untranslated regions. Meanwhile, extensive analyses of novel transcripts, ASs, and lncRNAs also revealed a large number of ASs and lincRNAs closely related to the regulation of characteristic secondary metabolites including catechins, theanine, and caffeine. Finally, a root-specific CsMYB6 was characterized to regulate theanine biosynthesis by genetic and molecular analyses. CsMYB6 directly bound to and activate the promoter of theanine synthetase gene (CsTSI). The study lays a foundation for the further investigation of metabolic genomics and regulation in tea plants.KRAS, the most common oncogenic driver in human cancers, is controlled and signals primarily through protein-protein interactions (PPIs). The interaction between KRAS and SOS1, crucial for the activation of KRAS, is a typical, challenging PPI with a large contact surface area and high affinity. Here, we report that the addition of only one atom placed between Y884SOS1 and A73KRAS is sufficient to convert SOS1 activators into SOS1 inhibitors. We also disclose the discovery of BI-3406. Combination with the upstream EGFR inhibitor afatinib shows in vivo efficacy against KRASG13D mutant colorectal tumor cells, demonstrating the utility of BI-3406 to probe SOS1 biology. These findings challenge the dogma that large molecules are required to disrupt challenging PPIs. Instead, a "foot in the door" approach, whereby single atoms or small functional groups placed between key PPI interactions, can lead to potent inhibitors even for challenging PPIs such as SOS1-KRAS.Ynamides are versatile building blocks in organic synthesis. However, the synthesis of amino acid-derived ynamides is difficult but in high demand. Herein, we disclose the copper-catalyzed Csp-N coupling of sulfonamide, including amino acid and peptide derivatives, to give ynamides by using alkynyl benziodoxolones with broad functional group tolerance under mild reaction conditions. The electron-rich bipyridine as a ligand and ethanol as solvent were used for the success of this reaction. The usefulness of the obtained amino acid-derived ynamide as building block was showcased by further derivatization to unique amino acid derivatives. https://www.selleckchem.com/products/ezm0414.html A control experiment to elucidate the mechanistic insight was also described.The study of ionic dynamics in solids is essential to understanding and developing modern energy technologies. Here we study the ionic dynamics of orthorhombic Na2MgSiO4, an interesting case of a polar stuffed-cristobalite-type structure that contains two inequivalent Na sites within the channels of the magnesium silicate tetrahedral framework. Its preparation by a solid-state reaction method favors the presence of ∼2% of Na vacancies, converting it into a pure Na ionic conductor with an optimized ionic conductivity of ∼10-5 S cm-1 at 200 °C. The macroscopic migration has been characterized through impedance spectroscopy and molecular dynamics simulation, which proves the pure Na ionic character of the compound through hopping between Na1 and Na2 sites, forming three-dimensional migration zigzag-shaped paths. High-resolution solid-state 23Na magic-angle-spinning (MAS) NMR spectroscopy is employed to characterize the local structure and microscopic dynamics of Na-ion transport in Na2MgSiO4. Remarkably, variable-temperature 23Na MAS NMR and two-dimensional exchange spectroscopy evidence for the first time a Na site exchange phenomenon at room temperature, which further triggers Na ionic conduction at elevated temperatures.Most therapeutic agents used for treating brain malignancies face hindered transport through the blood-brain barrier (BBB) and poor tissue penetration. To overcome these problems, we developed peptide conjugates of conventional and experimental anticancer agents. SynB3 cell-penetrating peptide derivatives were applied that can cross the BBB. Tuftsin derivatives were used to target the neuropilin-1 transport system for selectivity and better tumor penetration. Moreover, SynB3-tuftsin tandem compounds were synthesized to combine the beneficial properties of these peptides. Most of the conjugates showed high and selective efficacy against glioblastoma cells. SynB3 and tandem derivatives demonstrated superior cellular internalization. The penetration profile of the conjugates was determined on a lipid monolayer and Transwell co-culture system with noncontact HUVEC-U87 monolayers as simple ex vivo and in vitro BBB models. Importantly, in 3D spheroids, daunomycin-peptide conjugates possessed a better tumor penetration ability than daunomycin. These conjugates are promising tools for the delivery systems with tunable features.Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein-ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.